Ventilation system with automatic flow balancing derived from a neural network and methods of use

ABSTRACT

A ventilation system with automatic flow balancing derived from a neural network to consistently achieve a desired flow rate for inlet flow and/or outlet flow in various operating environments to optimize system performance. The system includes a ventilation device that includes an exhaust blower assembly with a blower motor and a control circuit having a mathematical equation that determines an estimated exhaust blower flow based upon select inputs. The ventilation device also includes a supply blower assembly with a blower motor and control circuit having a mathematical equation that determines an estimated supply blower flow based upon select inputs. When the estimated exhaust blower flow is different than an exhaust flow set point, the exhaust control circuit selectively alters power supplied to the exhaust motor. When the estimated supply blower flow is different than a supply flow set point, the supply control circuit selectively alters power supplied to the supply motor.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/438,066, filed on Jun. 11, 2019, which claims the benefit of U.S.Provisional Patent Application No. 62/683,420, filed on Jun. 11, 2018,which Applications are incorporated in their entirety herein byreference and made a part hereof.

FIELD OF DISCLOSURE

The present invention relates to a ventilation system, including aventilation device, such as an air exchanger, with automatic flowbalancing derived from a neural network to consistently achieve adesired flow rate for inlet flow and/or outlet flow in a variety ofoperating environments to optimize system performance.

BACKGROUND

FIG. 1A illustrates a partial cut-away view of a first operatingenvironment 105 that contains an exemplary ventilation system 100 havingan air exchanger 110 but that lacks an automatic flow balancing system.While the operating environment 105 shown in FIG. 1A is a house, theventilation system 100 and air exchanger 110 may be installed in anybuilding or structure including, but not limited to residential units,office buildings, factories, storage units, etc., to enable movement ofair (gas, fumes, airborne particulate matter, or etc.). In particular,the air exchanger 110 may be a heat recovery ventilator (“HRV”) orenergy recovery ventilator (“ERV”). An HRV or ERV 110 moves air from afirst space (e.g., outside the building) 115 to a second space (e.g.,inside the building) 145 along a supply flow path 120. For example, theHRV or ERV 110 unit moves air along the following path: (i) from thefirst space 115 into a supply duct 122, (ii) the center 125 of the HRVor ERV 110, (iii) through the supply blower 130, (iv) into the supplyducting in the operating environment 135, and (v) out of the supplyvents in the operating environment 140 to the second space 145.Additionally, the HRV or ERV 110 moves air from a second space (e.g.,inside the building) 145 to a first space (e.g., outside the building)115 along an exhaust flow path 150. For example, the HRV or ERV 110moves air along the following path: (i) from the second space 145 intoan exhaust ducting in the operating environment 155, (ii) the center 125of the HRV or ERV 110, (iii) through the exhaust blower 160, (iv) intothe exhaust duct 162, and (v) out of the exhaust vents in the operatingenvironment 165 to the first space 115.

Ventilation systems 100 can have various configurations depending on theoperating environment and the requirements of the system. For example,FIG. 1B illustrates a partial cut-away view of a second operatingenvironment 105 a that contains an exemplary ventilation system 100 ahaving both an air exchanger 110 a and an air handler or HVAC unit 170a, but the ventilation system 100 a lacks an automatic flow balancingsystem. In contrast with the operating environment 105 a shown in FIG.1A, an air handler or HVAC unit 170 a is positioned between the supplyducting in the operating environment 135 a and the supply vents in theoperating environment 140 a. The differences between the operatingenvironments 105 a shown in FIGS. 1A-1B illustrate that the operatingenvironment may require different duct lengths, transitions, and/or flowpath obstructions (e.g., filters or air handlers). These differences ininstallations create different air path restrictions, namely differentrestrictions on the supply flow path 120 a and the exhaust flow path 150a. Accordingly, the installer of the air exchanger 110 a must try tomanually adjust the system to account for these differences.

In an attempt to account for the differences between the differentinstallations, a complex and time consuming process, which is shown anddescribed in FIGS. 2A-2B, is undertaken by the installer. Specifically,in STEP 200, the installer will seal all the ductwork (e.g., see FIGS.1A-1B at 155, 155 a, 135, 135 a) within the house with tape and closeall windows and doors. In STEP 205, the installer will turn off allexhaust devices (e.g., range hood, dryer, and bathroom fan) 250, 250 a.Next, in STEP 210, the installer will ensure that the balancing damperslocated on the inlet ports (e.g., WE and CS) to the air exchanger 110,110 a are fully open. In STEP 215, the installer will set the airexchanger 110, 110 a to its highest speed. Next, in STEP 220, theinstaller places the pressure gauges 255 on a level surface and adjustthe gauges to zero. In STEP 225, the installer connects the pressuregauges 255 to the air exchanger 110, as shown in FIG. 2A. Next, in STEP230, the installer determines the pressure value associated with theselected CFM value for the specific installation from the balancingchart provided with the air exchanger 110, 110 a. In STEP 235, theinstaller adjusts the balancing dampers located on the inlet ports(e.g., WE and CS) until the values displayed on the pressure gauges 255substantially match the values associated with the selected CFM value.This process may need to be repeated multiple times before the airexchanger 110, 110 a is successfully balanced. Additionally, even if theair exchanger 110, 110 a is properly balanced at one point, the airexchanger 110, 110 a may become unbalanced due to changes in outside airdensity (e.g., changes in air temperature during the summer and winter),between cleaning or replacement of the air filters, or high wind speedin high rise towers.

An unbalanced air exchanger 110, 110 a not only will have a degradedperformance, but it will also cause multiple other problems depending onhow the air exchanger 110, 110 a is unbalanced. For example, if the airexchanger 110, 110 a is unbalanced in a manner that creates positive airpressure in the operating environment 105, as shown in FIG. 3A, the airexchanger 110, 110 a will push hot and/or humid air into the wallsand/or insulation. This, in turn, can lead to mold, mildew, and/or rotforming in the walls. Additionally, this leads to heat loss within theoperating environment 105, 105 a. Alternatively, if the air exchanger110, 110 a is unbalanced in a manner that creates negative air pressurein the operating environment 105, as shown in FIG. 3B, the air exchanger110, 110 a will force unconditioned air within the operating environment105, 105 a. This, in turn, can lead to mold forming in the walls andwill increase energy costs. Further, this may create backdrafts fromcombustion applications. To avoid these multiple problems, the airexchanger 110, 110 a should be properly balanced within the operatingenvironment 105, 105 a. Moreover, an unbalanced air exchanger 110, 110 amay not meet the building codes.

Accordingly, an air exchanger that overcomes the above issues isdescribed herein. Specifically, the ventilation device 310 describedherein automatically balances. Additionally, the ventilation device 310has the ability to rebalance itself in light of temperature changes orother like factors. Further, an ventilation device 310 that canautomatically balance itself within 10% error. Such an ventilationdevice 310 will save the installer from performing all the STEPSdescribed in FIG. 2A at various points, including at installation andwhen the seasons change. In addition, such an ventilation device 310will meet various building codes (see e.g., Canada's National buildingcodes, including 9.32.3.4-9.32.3.5, and CAN/CSA-F326-M91), includingstricter local codes that have been or may be adopted in the future(e.g., State of California).

The description provided in the background section should not be assumedto be prior art merely because it is mentioned in or associated with thebackground section. The background section may include information thatdescribes one or more aspects of the subject technology.

SUMMARY

The present disclosure relates to a ventilation system, including aventilation device, such as an air exchanger, with automatic flowbalancing derived from a neural network to consistently achieve adesired flow rate for inlet flow and/or outlet flow in a variety ofoperating environments to optimize system performance.

According to an aspect of the present disclosure, the present inventionprovides a ventilation system with automatic flow balancing derived froma neural network for installation in a ventilation environment. Theventilation system includes ventilation device lacking a pressuresensor, but includes a first blower assembly including a blower motorand a control circuit, said control circuit having a first mathematicalequation. The first mathematical equation that is contained within thefirst blower assembly determines an estimated blower air flow for thefirst blower assembly based upon the following inputs: (i) exhaust airpath parameters derived from the use of a neural network, (ii) blowermotor speed, and (iii) blower motor current. The ventilation system thendetermines if the blower air flow is different than an air flow setpoint determined by a user of the system, the control circuit beingconfigured to selectively alter power supplied to the blower motor inorder to make the estimated blower air flow equal to the air flow setpoint.

According to an aspect of the present disclosure, the present inventionprovides a ventilation system with automatic flow balancing derived froma neural network for installation in a ventilation environment. Theventilation system includes ventilation device lacking a pressuresensor, but includes a supply blower assembly including a supply blowermotor and a supply control circuit, said supply control circuit having asupply mathematical equation. The ventilation system also includes anexhaust blower assembly including an exhaust blower motor and an exhaustcontrol circuit, said exhaust control circuit having an exhaustmathematical equation. The supply mathematical equation that iscontained within the supply blower assembly determines an estimatedsupply blower air flow for the supply blower assembly based upon thefollowing inputs: (i) supply air path parameters derived from the use ofa neural network, (ii) supply blower motor speed, and (iii) supplyblower motor current. The exhaust mathematical equation that iscontained within the exhaust blower assembly determines an estimatedexhaust blower air flow for the exhaust blower assembly based upon thefollowing inputs: (i) exhaust air path parameters derived from the useof a neural network, (ii) exhaust blower motor speed, and (iii) exhaustblower motor current. The ventilation system then determines if thesupply blower air flow is different than an supply air flow set pointdetermined by a user of the system, the supply control circuit beingconfigured to selectively alter power supplied to the supply blowermotor in order to make the estimated supply blower air flow equal to thesupply air flow set point. And finally, the ventilation system thendetermines if the exhaust blower air flow is different than an exhaustair flow set point determined by a user of the system, the exhaustcontrol circuit being configured to selectively alter power supplied tothe exhaust blower motor in order to make the estimated exhaust blowerair flow equal to the exhaust air flow set point.

Other aspects and advantages of the present disclosure will becomeapparent upon consideration of the following detailed description andthe attached drawings wherein like numerals designate like structuresthroughout the specification.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide furtherunderstanding and are incorporated in and constitute a part of thisspecification, illustrate disclosed embodiments and together with thedescription serve to explain the principles of the disclosedembodiments. In the drawings:

FIG. 1A is a partially cut-away view of a first exemplary ventilationsystem containing the air exchanger unit;

FIG. 1B is a partially cut-away view of a second exemplary ventilationsystem containing the air exchanger unit;

FIG. 2A is the air exchanger unit undergoing a manual balancingprocedure;

FIG. 2B is an exemplary flowchart of the steps taken to manually balancethe air exchanger unit;

FIG. 3A is a view of an exemplary ventilation system that has a positiveair pressure imbalance;

FIG. 3B is a view of an exemplary ventilation system that has a negativeair pressure imbalance;

FIG. 4A is a block diagram illustrating a first exemplary embodiment ofa ventilation system in an operating environment;

FIG. 4B is a perspective view showing components of the ventilationdevice of the system FIG. 4A;

FIGS. 5A-5C are block diagrams of the control circuit for theventilation device unit shown in FIG. 4 ;

FIGS. 6A-6B are exemplary flowcharts showing the operation of the flowcontroller;

FIG. 7 is an exemplary test setup for measuring parameters that areassociated with the ventilation device shown in FIG. 4 ;

FIG. 8A is a graph containing actual measurements of the air flow andthe static pressure of the ventilation device, shown in FIG. 4 , for alloperating points;

FIG. 8B is a graph containing actual measurements of the motor currentand the motor rotational speed of the ventilation device, shown in FIG.4 , for all operating points shown in FIG. 7 ;

FIG. 9 is a graph containing actual measurements and virtualmeasurements of the motor current and the motor rotational speed of theventilation device, shown in FIG. 4 , for all operating points shown inFIG. 7 ;

FIG. 10 is a graph showing the estimated limits of the motor current andthe motor rotational speed of the ventilation device, shown in FIG. 4 ,within which the mathematical equation can estimate the air flow;

FIGS. 11A-11B are blower maps showing air flow, motor current, and motorrotational speed, for all operating points shown in FIG. 7 , of theventilation device shown in FIG. 4 ;

FIG. 12 shows a method of determining a mathematical equation using theblower maps from FIGS. 11A-11B to train a neural network to estimate theair flow from the ventilation device shown in FIG. 4 ;

FIG. 13 is an exemplary flowchart showing the determination of themathematical equation and its associated air path parameters;

FIG. 14A provides contains a graph that shows the percentage of air flowestimation errors, at all operating air flow rates shown in FIG. 7 , andapplicable error limits for the ventilation device shown in FIG. 4 ;

FIG. 14B provides contains a graph that shows the air flow estimationerrors, at all operating air flow rates shown in FIG. 7 , and applicableerror limits for the ventilation device shown in FIG. 4 ;

FIG. 15A provides contains a graph that shows the percentage of air flowestimation errors, at all operating air flow rates shown in FIG. 7 , andapplicable error limits for an air exchanger that utilizes a polynomialequation;

FIG. 15B provides contains a graph that shows the air flow estimationerrors, at all operating air flow rates shown in FIG. 7 , and applicableerror limits for the air exchanger that utilizes the polynomialequation;

FIG. 16 provides a graph showing the distribution of the percentage ofthe air flow estimation errors shown in FIG. 14A;

FIG. 17 provides a graph showing the distribution of the percentage ofthe air flow estimation errors shown in FIG. 15A;

FIGS. 18-19 provides graphs comparing the performance of the ventilationdevice shown in FIG. 4 and the air exchanger that utilizes thepolynomial equation;

FIGS. 20A-20B are circuit diagrams of the control circuit for a secondexemplary embodiment of an ventilation device according to the presentdisclosure; and

FIG. 21 is circuit diagrams of the control circuit for a third exemplaryembodiment of an ventilation device according to the present disclosure.

In one or more implementations, not all of the depicted components ineach figure may be required, and one or more implementations may includeadditional components not shown in a figure. Variations in thearrangement and type of the components may be made without departingfrom the scope of the subject disclosure. Additional components,different components, or fewer components may be utilized within thescope of the subject disclosure.

DETAILED DESCRIPTION

The detailed description set forth below is intended as a description ofvarious implementations and is not intended to represent the onlyimplementations in which the subject technology may be practiced. Asthose skilled in the art would realize, the described implementationsmay be modified in various different ways, all without departing fromthe scope of the present disclosure. Further, modules and processesdepicted may be combined, in whole or in part, and/or divided, into oneor more different parts, as applicable to fit particular implementationswithout departing from the scope of the present disclosure. Moreover,disclosure of structures, functions, operations, components from oneembodiment may be utilized in connection with or may replace structures,functions, operations, components contained within a differentembodiment. Accordingly, the drawings and description are to be regardedas illustrative in nature and not restrictive.

FIG. 4A is a block diagram illustrating a first exemplary embodiment ofa ventilation system 300 installed an operating environment 305. FIG. 4Bshows components of a ventilation device 310 of the system 300. Theventilation device 310, may be an HRV or ERV, is designed to move airfrom one location to another location within the operating environment305. The system 300, including the ventilation device 310, can beinstalled in a variety of environments 305, such as buildings orstructures including houses, single family or multi-family residentialunits, office buildings, factories, manufacturing facilities, etc.Accordingly, the system 300 includes: (i) the ventilation device 310,(ii) a supply flow path 320, which allows air to move from the firstspace (e.g., outside the environment 305) to the second space (e.g.,inside the environment 305), (iii) an exhaust flow path 350, whichallows air to move from the second space (e.g., inside the environment305) to the first space (e.g., outside the environment 305), and (iv)optional components, such as temperature sensor 375, relative humidity380, and motorized damper 395.

The ventilation device 310 is designed to replace the air exchanger 110shown in FIGS. 1A-1B. The ventilation device 310 includes a supplyblower 330 and an exhaust blower 360, which are controlled by a controlcircuit 340. While both blowers 330, 360 may be permanent magnetsynchronous motor (PMSM) or a brushless AC (BLAC), it should beunderstood that different types of blowers 330, 360 may be used, such asa brushless DC motor (BLDC). The ventilation device 310 does not includea flow or pressure sensor within the core 325 or within the adjacent airpaths 320, 350 for measuring and adjusting the blowers 330, 360. Notutilizing a flow or pressure sensor reduces the cost of the ventilationdevice 310 and increases reliability due to the fact that this failurepoint is eliminated. Instead, the ventilation device 310 uses thecontrol circuit 340 in connection with a mathematical solution 500 a,500 b to automatically balance the supply and exhaust air flow paths inreal time. For example, the ventilation device 310 may include a supplyflow estimator 455 a that includes a mathematical equation 500 a thatutilizes supply air path parameters 415 that are derived from a neuralnetwork that is trained using a supply blower map. Likewise, theventilation device 310 may include an exhaust flow estimator 455 b thatincludes a mathematical equation 500 b that utilizes supply air pathparameters 425 that are derived from a neural network that is trainedusing an exhaust blower map. The mathematical solutions 500 a, 500 b maybe varied based upon the complexity ventilation device 310, its desiredoperating parameters, and ambient conditions which are understood tovary due to seasonal changes. Accordingly, the ventilation device 310does not have to be manually balanced, as discussed in FIGS. 2A-2B,which provides significant cost savings and improved efficiencies to thesystem 300. Additionally and as discussed in greater detail below, themathematical solution 500 a, 500 b derived from the use of a neuralnetwork and that is implemented within the control circuit 340 of theventilation device 310 provides close to a tenfold (10 x) improvementover the use of certain polynomial equations used in conventional airexchangers 110. The system's 300 use of the mathematical solution 500 a,500 b derived from a neural network consistently achieves a desired flowrate for inlet flow and/or outlet flow in a variety of operatingenvironments to optimize system performance. The use of the mathematicalsolution 500 a, 500 b derived from a neural network in the ventilationdevice 310 solves the significant inability of conventional airexchangers 110 using polynomial equations to meet building coderequirements that are being implemented globally.

FIGS. 5A-5C are circuit diagram of the control circuit 340 for theventilation device 310, shown in FIG. 4 . The control circuit 340receives input signals from a supply air flow set point 365, an exhaustair flow set point 370, an optional temperature sensor 375, and anoptional relative humidity 380. The combination of the supply air flowset point 365 and an exhaust air flow set point 370 is a user definedair flow set point 364, which is set at a second location, such as ahouse, apartment, or office. The user may set the user defined air flowset point 364 using a controller to select the desired CFM output of thesystem 300 into the environment 305. It should be understood that thesupply air flow set point 365 may be different than the exhaust air flowpoint 370 for the user defined air flow set point 364. The controllermay be: (i) a wall controller that is coupled to the ventilation device310 using a wire, (ii) a wall controller that is coupled to theventilation device 310 using a wireless connection (e.g., Bluetooth,Wi-Fi), (iii) a mobile device that is wirelessly coupled to theventilation device 310, or (iv) a combination of any of thesecontrollers. For example, the user may set the user defined air flow setpoint 364 to a value between 40 CFM to 300 CFM depending on theoperating environment 305. The control circuit 340 also send outputsignals to: (i) a supply blower motor 385, which is a part of the supplyblower 330, and (ii) an exhaust blower motor 390, which is a part of theexhaust blower 360. Optionally, the control circuit 340 can also sendoutput signals to a motorized damper 395, such as the damper disclosedwithin Ser. No. 16/242,498, filed on Jan. 9, 2019, and which is herebyincorporated by reference for all purposes. This damper may be used toincrease the internal restriction of the ventilation device 310 to bringthe pressure within the flow estimation operating limits.

The following description describes components contained within thecontrol circuit 340. It should understood that the following componentsthat have similar numbers that are only separated by “a” and “b” arecomponents that are substantially similar. For example, a component thatis located on the supply side of the ventilation device 310 will bedenoted with an “a”, while the same component that is located on theexhaust side of the ventilation device 310 will be denoted with a “b”.The components containing control circuit 340 include: (i) productcontrol logic board 405, (ii) supply flow controller 410 and (iii)exhaust flow controller 420. The supply flow controller 410 includessupply air path parameters 415, supply flow estimator 455 a, inverter456 a and a phase current measurement component 461 a. Like the supplyflow controller 410, the exhaust flow controller 400 includes exhaustair path parameters 425 b, exhaust flow estimator 455 b, inverter 456 band a phase current measurement component 461 b. The supply and exhaustflow controllers each include separate air path parameters 415, 425 andseparate flow estimators 410, 420 to ensure that the blower motor 385,390 can adjust the air flow through their respective flow path 320, 350to account for differences (e.g., duct length, filters, or otherobstructions) between the flow paths 320, 350. Such differences areshown and discussed in connection with FIGS. 1-1A. For example, thesupply flow path 320 may be more restrictive than the exhaust flow path350 because the supply flow path 320 may contain additional filters.Additionally, the supply flow path 320 and the exhaust flow path 350 mayhave different static pressures because of an air handler 170.Accordingly, each blower motor's controller 410, 420 accounts for thesedifferences to ensure that the ventilation device 310 is balanced.

FIG. 5A-5C are circuit diagram containing components of the ventilationdevice 310, shown in FIG. 4 , including supply flow controller 410 andexhaust flow controller 420. The supply and exhaust flow controller 410,420 each contain approximately three cascading feedback loops that worktogether to regulate the air flow produced by the blower 330, 360. Inparticular, the feedback loops regulate the current that is supplied tothe blower 330, 360, which in turn regulates the air flow produced bythe blower 330, 360. Here, the regulation of the current supplied to theblower motor 385, 390 dictates the speed at which the blower motor 385,390 turns the fan blades. Also, the regulation of the speed that theblower motor 385, 390 turns the fan blades dictates the air flowproduced by the blower 330, 360. Therefore, the regulation of thecurrent being supplied to the blower 330, 360 controls the air flowproduced by the blower 330, 360. It should be understood, that therelationship between the current being supplied to the blower 330, 360does not have a linear relationship to the air flow produced by theblower 330, 360. To try to account for this non-linear relationship,this disclosure discusses the utilization of a mathematical solution 500a, 500 b derived from a neural network in order to determine therelationship between the current, motor rotation, and the air flow rate.Additionally, it should be understood that the non-linear relationshipof the current in comparison with the air flow produced by the supplyblower 330 may be different than the non-linear relationship of thecurrent in comparison with the air flow produced by the exhaust blower360. To account for these differences between the supply and exhaustpaths, different mathematical solutions 500 a, 500 b are utilized foreach air path. In particular, the supply side blower 330 will becontrolled using the supply mathematical solution 500 a and the exhaustside blower 360 will be controlled using the exhaust mathematicalsolution 500 b.

The circuit diagrams shown in FIGS. 5A-5C operate in the followingmanner. The mathematical equation 500 a, 500 b determines an estimatedblower air flow 427 a, 427 b for the blower assembly 330, 360 based uponthe following inputs: (i) air path parameters 415, 425 derived from theuse of a neural network, (ii) blower motor speed 439 a, 439 b, and (iii)blower motor current 457 a, 457 b. If an estimated air flow rate 427 a,427 b (e.g., 90 CFM) is below a desired air flow set point 365, 370(e.g., 100 CFM), then the flow controller 410, 420 increases the speeddemand, which in turn will increase the current that is supplied to theblower motor 385, 390. The increase in current being supplied to theblower motor 385, 390 causes an increase in the speed of the blowermotor 385, 390, which in turn increases the air flow produced by theblower 330, 360. The cascading feedback loops continue to work togetherto increase the current being supplied to blower motor 385, 390 untilthe blower motor's speed is fast enough to provide air flow at thedesired flow set point (e.g., 100 CFM). It should be understood that anincrease in the current being supplied to one of the blowers 330, 360may require a similar increase, less of an increase, or more of anincrease in the current being supplied to the other blower 330, 360 toaccount for the differences in the air flow paths 320, 350 and maintainbalance between the air flows 320, 350.

Alternatively, if the estimated air flow rate 427 a, 427 b (e.g., 110CFM) is above a desired air flow set point 365, 370 (e.g., 100 CFM),then the flow controller 410, 420 decreases the speed demand, which inturn decreases the current that is supplied to the blower motor 385,390. The decrease in current being supplied to the blower motor 385, 390causes a decrease in the speed of the blower motor 385, 390. Thisdecrease in the speed of the blower motor 385, 390 results in a decreasein the air flow produced by the blower 330, 360. The cascading feedbackloops continue to work together to decrease the current being suppliedto the blower motor 385, 390 until the blower motor's speed is slowenough to provide air flow at the desired flow set point (e.g., 100CFM). As described above, a decrease in the current being supplied toone of the blowers 330, 360 may require a similar decrease, less of adecrease, or more of a decrease in the current being supplied to theother blower 330, 360 to account for the differences in the air flowpaths 320, 350. To protect the blower 330, 360 from major damage, theflow controller 410, 420 limits: (i) the amount of current that can beapplied to the blower motor 385, 390 and (ii) the blower motor's 385,390 RPMS. If the air flow set point requires the blower 330, 360 tooperate outside of the current limits, the control circuit 340 willprovide the user or installer with a warning that the desired air flowset point 415, 425 cannot be reached by the system.

Specifically, the functionality of the cascading feedback loops that arecontained within each of the flow controller 410, 420 is describedbelow. STEPS 700-734 describe the flow of the first feedback loop 401 a,401 b, STEPS 706-728 describe the flow of the second feedback loop 402a, 402 b, and STEPS 714-724 describe the flow of the third feedback loop403 a, 403 b. In STEP 700, a first error calculator 429 a, 429 bdetermines an air flow error 431 a, 431 b based on the air flow setpoint 365, 370 and an estimated blower air flow 427 a, 427 b. In STEP702, the resulting air flow error 431 a, 431 b is fed into an air flowcontroller 433 a, 433 b. In STEP 704, the air flow controller 433 a, 433b uses a proportional integral derivative (“PID”) or other optimizedmethods to compute the speed set point 435 a, 435 b. In STEP 706, asecond error calculator 441 a, 441 b determines a speed error 437 a, 437b based on the speed set point 435 a, 435 b and motor estimatedrevolutions per minute (“RPM”) 439 a, 439 b. In STEP 708, the resultingspeed error 437 a, 437 b is fed into a speed controller 443 a, 443 b. InSTEP 710, the speed controller 443 a, 443 b uses a PID or otheroptimized methods to compute the motor current set point 445 a, 445 b.In STEP 712, the motor current set point 445 a, 445 b is feed into afield oriented control (“FOC”) 447 a, 447 b. The FOC 447 a, 447 b isused to regulate the motor phase current 449 a, 449 b and may includetwo calculators inverse park transform component, a park transform,Clarke transform, torque controller, and/or a flux controller. In STEPS714 and 716, the FOC 447 a, 447 b regulates the motor phase current 449a, 449 b by adjusting the motor voltage set point 451 a, 451 b that isfed into the motor pulse-width modulation (“PWM”) generator 453 a, 453 bbased on the received motor current set point 445 a, 445 b. The PWMgenerator 453 a, 453 b may use: (i) a space vector modulation (“SVM”)technique, or (ii) SPWM (“Sinusoidal PWM”) technique, or (iii) othersimilar techniques to optimize the system performance and improve usageof the DC bus voltage. In STEP 718, the PWM generator 453 a, 453 b feedsthe modulated signal 454 a, 454 b into the power supply components 397,397 a; specifically, the inverter power stage 456, 456 a. In STEP 720,the inverter power stage 456 a, 456 b supplies power to the blower motor385, 390.

In STEP 722, a phase current measuring module 461 a, 461 b measures andoutputs the motor phase current 449 a, 449 b. In STEP 724, the motorphase current 449 a, 449 b is then fed into the FOC controller 447 a,447 b and a position estimator 459 a, 459 b. The FOC controller 447 a,447 b utilizes the measured motor phase current 449 a, 449 b todetermine what future adjustments to the motor voltage set point 451 a,451 b are required to meet the desired air flow set point 365, 370. InSTEP 726, the position estimator 459 a, 459 b uses the measured motorphase current 449 a, 449 b to compute the motor estimated RPM 439 a, 439b. To compute the motor estimated RPM 439 a, 439 b, the positionestimator 459 a, 459 b uses either: (i) a phase locked loop (“PLL”)observer, (ii) a high frequency signal injection (“HFI”) observer, (iii)Cordic observer, or (iv) a position sensor, or (v) other positionobserver relevant for the application.

In STEP 728, the motor estimated RPM 439 a, 439 b is fed into the seconderror calculator 441 a, 441 b and into an air flow estimator 455 a, 455b. In STEP 730, the FOC controller 447 a, 447 b uses a Park and Clarktransform to analyze the motor phase current 449 a, 449 b in order todetermine the motor estimated current 457 a, 457 b. In STEP 732, themotor estimated current 457 a, 457 b is fed into the air flow estimator455 a, 455 b. In STEP 734, the air flow estimator 455 a, 455 bcalculates the estimated air flow 427 a, 427 b using the mathematicalequation 500 a, 500 b. Specifically, the mathematical equation 500 a,500 b uses: (i) the motor estimated RPM 439 a, 439 b, (ii) the motorestimated current 457 a, 457 b and (iii) air path parameters 415, 425 tocompute the blower estimated air flow 427 a, 427 b. STEPS 700-734 arecontinually repeated while the blower 330, 360 is operating; this helpsto ensure that the air flow remains at the air set point 365, 370.Specifically, STEPS 700-734 are preferably repeated at least every fiveseconds, more preferably every two seconds, and most preferably everyhalf of a second.

The operation of the second feedback loop 402 a, 402 b is describedbelow. The second error calculator 441 a, 441 b determines a speed error437 a, 437 b based on the speed set point 435 a, 435 b and motorestimated RPM 439 a, 439 b. In STEP 708, the resulting speed error 437a, 437 b is fed into a speed controller 443 a, 443 b. In STEP 710, thespeed controller 443 a, 443 b uses a PID or other optimized methods tocompute the motor current set point 445 a, 445 b. In STEP 712, the motorcurrent set point 445 a, 445 b is fed into a FOC 447 a, 447 b. The FOC447 a, 447 b is used to regulate the motor phase current 449 a, 449 b.In STEPS 714 and 716, the FOC 447 a, 447 b, regulates the motor phasecurrent 449 a, 449 b by adjusting the motor voltage set point 451 a, 451b that is fed into the motor PWM generator 453 a, 453 b based on thereceived motor current set point 445 a, 445 b. In STEP 718, the PWMgenerator 453 a, 453 b feeds the modulated signal 454 a, 454 b into thepower supply components 397 a, 397 b; specifically, the inverter powerstage 456 a, 456 b. In STEP 720, the inverter power stage 456 a, 456 bsupplies power to the blower motor 385, 390. In STEP 722, a phasecurrent measuring module 461 a, 461 b measures and outputs the motorphase current 449 a, 449 b. In STEP 724, the motor phase current 449 a,449 b is then fed into the FOC controller 447 a, 447 b and a positionestimator 459 a, 459 b. In STEP 726, the position estimator 459 a, 459 buses the measured motor phase current 449 a, 449 b to compute the motorestimated RPM 439 a, 439 b. In STEP 728, the motor estimated RPM 439 a,439 b is fed into the second error calculator 441 a, 441 b and into anair flow estimator 455 a, 455 b. STEPS 706-728 are continually repeatedwhile the blower 330, 360 is operating; this helps to ensure that theair flow remains at the air set point 365, 370. Specifically, STEPS706-728 are preferably repeated at least every half second, morepreferably every quarter second, and most preferably every millisecond.

The operation of the third feedback loop 403 a, 403 b is describedbelow. The FOC 447 a, 447 b is used to regulate the motor phase current449 a, 449 b. In STEPS 714 and 716, the FOC 447 a, 447 b regulates themotor phase current 449 a, 449 b by adjusting the motor voltage setpoint 451 a, 451 b that is fed into the motor PWM generator 453 a, 453 bbased on the received motor current set point 445 a, 445 b. In STEP 718,the PWM generator 453 a, 453 b feeds the modulated signal 454 a, 454 binto the power supply components 397 a, 397 b; specifically, theinverter power stage 456 a, 456 b. In STEP 720, the inverter power stage456, a 456 b supplies power to the blower motor 385, 390. In STEP 722, aphase current measuring module 461 a, 461 b measures and outputs themotor phase current 449 a, 449 b. In STEP 724, the motor phase current449 a, 449 b is then fed into the FOC controller 447 a, 447 b and aposition estimator 459 a, 459 b. STEPS 714-724 are continually repeatedwhile the blower 330, 360 is operating; this helps to ensure that theair flow remains at the air set point 365, 370. Specifically, STEPS714-724 are preferably repeated at least every microsecond, morepreferably every quarter microsecond, and most preferably everymillisecond. In other words, STEPS 714-724 are performed typicallybetween 4,000 to 16,000 time a sec.

FIGS. 7-11B generally show a method of configuring a ventilation systemwith automatic flow balancing derived from a neural network. TheseFigures show and describe the steps that are undertaken to generate thesupply and exhaust mathematical equation 500 a, 500 b and the associatedsupply and exhaust air path parameters 415, 425. The ventilation device310 is first set up in a test environment 600 at a first location (e.g.,at a manufacture's warehouse). See FIG. 13 at STEP 900. The testenvironment includes the ability to vary the current and voltage that isapplied to the supply and exhaust blower 330, 360 using a currentregulator 602 a, 602 b and a voltage regulator 604 a, 604 b. Inaddition, this test environment includes a static pressure sensor 606 a,606 b to measure the static pressure between one and four feet from theair outlet of the ventilation device 310, and preferably two feet fromthe air outlet of the ventilation device 310. Further, this testenvironment includes an air flow sensor 608 a, 608 b to measure the airflow rate between four and ten feet from the air outlet of theventilation device 310, and preferably six feet from the air outlet ofthe ventilation device 310. Also, this test environment may includeadditional modifying devices or components 612 a, 612 b to alter orchange the air temperature, alter or change the relative humidity,change the static pressure, or modify the air flow path usingobstructions (e.g., filters).

Once the ventilation device 310 is set up in a test environment 600, theventilation device 310 is operated at various levels and measurementsare recorded for both the supply air path 320 and the return 350. SeeFIG. 13 at STEP 902. For example, each blower motor 330, 360 may beoperated at a predefined set of current levels and measurements (e.g.,air flow, motor speed) can be taken at each predefined set of currentvalues. Specifically, these measurements may include the blowers 330,360 rotational speed (S₁₋₂₁₀), current (C₁₋₂₁₀), air flow (F₁₋₂₁₀), andstatic pressure (P₁₋₂₁₀). An exemplary table is shown below.

Flow Pressure Speed Current 201 P₁  S₁  C₁  . . . 181.5 P₂₂  S₂₂  C₂₂  .. . 165.5 P₄₃  S₄₃  C₄₃  . . . 142 P₆₄  S₆₄  C₆₄  . . . 121 P₈₅  S₈₅ C₈₅  . . . 100 P₁₀₆ S₁₀₆ C₁₀₆ . . . 79 P₁₂₇ S₁₂₇ C₁₂₇ . . . 60 P₁₄₈ S₁₄₈C₁₄₈ . . . 37 P₁₆₉ S₁₆₉ C₁₆₉ . . . 26.5 P₁₉₀ S₁₉₀ C₁₉₀ . . . 25.5 P₂₁₀S₂₁₀ C₂₁₀It should be understood that other and/or additional measurements may betaken in this test environment. For example, measurements may includethe following: i) PWM signal, ii) static pressure at all four ports ofthe ventilation device (i.e., CS, WS, WE, CE), iii) air flow at all fourports of the ventilation device (i.e., CS, WS, WE, CE), iv) supply andexhaust pressure in the core, v) supply and exhaust blower motor rpms,vi) input power to the ventilation device 310, vii) input voltage to theventilation device 310, viii) input current to the ventilation device310, ix) input frequency to the ventilation device 310, x) supply andexhaust blower motor flux, xi) supply and exhaust blower motor current,and xii) supply and exhaust blower motor power, and etc. Further, it maybe desired to take all of these measurements at various temperatures andwith the heat recovery core in various operating states due to the factthat both temperature and the operation of the heat recovery core mayaffect the balance of the ventilation device 310. It should further beunderstood that the number of measured points may range from 100 to10000, preferably between 150 and 500, and most preferably between 200and 300.

Once the measurements have been recorded at various operating points inSTEP 902, multiple graphs may be generated from these measurements,including graphs for the supply path 320 and the exhaust path 350. SeeFIG. 13 at STEP 904. In particular, each of the flowing steps may beperformed a first time for the supply path 320 to create a supply blowermap that can be utilized to train a supply neural network and the stepscan be performed a second time for the exhaust path 350 to create anexhaust blower map that can be utilized to train an exhaust neuralnetwork. Once the graphs are created, a system designer (e.g., anemployee of the manufacturer of the system 300, who may be located at afirst location) may review them to ensure that the data appears accuratein order to determine if the tests need to be rerun due to some error inthe setup. Two of these graphs that do not contain errors are shown inFIGS. 8A-8B. Specifically, FIG. 8A is a graph that maps the staticpressure measurements against the air flow measurements that were takenat various operating points. FIG. 8B is a graph that maps the motorrotational speed measurements against the motor current measurementsthat were taken at various operating points.

Once the graphs have been created and it has been confirmed that therewere no errors in the measurements, a computer program can be used togenerate virtual measurements. See FIG. 13 at STEP 906. Specifically,FIG. 9 shows the measured points 1000, while adding in the virtualpoints 1005. To calculate the values of these virtual points, a computeruses two dimensional (“2D”) linear interpolation fitting function. Thesystem designer, who is located at first location, at a manufacture'swarehouse, may select how many virtual points 1050 are added between themeasured points 1000. For example, system designer may desire togenerate over 10,000 virtual points from the 200 measured points. Thisprocess helps to ensure that there are enough total points to accuratelyperform the steps described below without taking the time to record over10,000 points. It should be understood that the ratio of virtual points1005 to measured points 1000 should be properly selected. This isbecause if there are too few measured points 1000 in comparison to thevirtual points 1050, the virtual points 1005 may not accurately reflectthe operational parameters of the ventilation device 310. On the otherhand, if there are not enough overall points (i.e., virtual points 1005and measured points 1000), the following steps may not generate themathematical equations 500 a, 500 b that accurately reflects theoperational parameters of the ventilation device 310.

Once the measured points and the virtual points have been combined, thesystem designer defines the operating limits of the ventilation device310. See FIG. 13 at STEP 908. Specifically, these limits, shown in athick red line 1010 (FIG. 10 ), are set using a second order polynomialfunction that connects the outer most measured points of the ventilationdevice 310. Also, these limits 1010 help ensure that the mathematicalequation can properly estimate the blower air flow 427 a, 427 b. If anyvirtual points 1005 or measured points 1000 are located outside of theoperational limits 1010, they will be removed from the graphs andfollowing calculations. This removal ensures that the outliers of thepoints 1000, 1005 do not skew the results of the mathematical equation.

The operational limits 1010, virtual points 1005, and measured points1000 which are then analyzed by a computer to generate a blower map1020. See FIG. 13 at STEP 910. As discussed above, a first or supplyblower map may be created for the supply path 320 and a second orexhaust blower map may be created for the exhaust path 350.Specifically, the computer uses an interpolant fitting function, such asa three dimensional (“3D”) cubic spline interpolation or a threedimension linear interpolation (“3D”), to generate a surface 1015 thatcorresponds to the measured points 1000 and virtual points 1005 that arecontained within the operating limits. FIGS. 11A-11B show the generatedthree-dimensional graph of a blower map 1020 that includes the surface1015 that was generated by the interpolating function. In particular,the blower map 1020 displayed in FIGS. 11A-11B maps motor rotationalspeed and motor current against air flow. By fitting the virtual points1005 and measured points 1000 with this surface, an infinite amount ofpoints have been created, which in turn will allow the mathematicalequation 500 a, 500 b to be able to estimate the air flow based on anycurrent and speed of the blower motor 385, 390. In an alternativeembodiments, measured points 1000, without virtual points 1005, may befitted with the interpolant fitting function to generate the blower map1020. In further embodiments, the surface 1015 of the blower map 1020may be created based upon the measured points 1000 without generatingthe virtual points 1005. In even further embodiments, a smoothingfunction may be applied to the measured points 1000 or a combination ofthe measured points 1000 and the virtual points 1005 before forming thesurface 1015 of the blower map 1020. In another embodiment, the bowermap may be: (i) a combination of the virtual points 1005 and measuredpoints 1000 without a surface 1015, (ii) the virtual points 1005 withouta surface 1015, or (iii) the measured points 1000 without a surface1015. Even further, combinations of these alternative embodiments may beused.

Once the blower map 1020 is generated for the supply path 320 and theexhaust path 350, the system designer can generate mathematicalequations 500 a, 500 b for use in the respective neural networkestimators 455 a, 455 b. See FIG. 13 at STEP 912. The supply and exhaustflow estimators 445 a, 445 b utilize the following flow estimationequation Flow(X)=Sig(Sig(Sig(X·W1+B1)·W2+B2)·W3+B3). In this equation,“X” is an input matrix (i.e., X=[Speed Current]) that includes theestimated RPM 439 a, 439 b and the motor estimated current 457 a, 457 b.In addition, “W” and “B” are weighted matrices (e.g., W₁-W₃) and thebias matrices (e.g., B₁-B₃), shown below.

$\begin{matrix}{W_{1} = \begin{bmatrix}w_{11} & \cdots & w_{61} \\ \vdots & \ddots & \vdots \\w_{12} & \cdots & w_{62}\end{bmatrix}} & {B_{1} = \begin{bmatrix}b_{1} & \cdots & b_{6}\end{bmatrix}} \\{W_{2} = \begin{bmatrix}w_{11} & \cdots & w_{61} \\ \vdots & \ddots & \vdots \\w_{16} & \cdots & w_{66}\end{bmatrix}} & {B_{2} = \begin{bmatrix}b_{1} & \cdots & b_{6}\end{bmatrix}} \\{W_{3} = \begin{bmatrix}w_{1} \\ \vdots \\w_{6}\end{bmatrix}} & {B_{3} = \left\lbrack {b1} \right\rbrack}\end{matrix}$

To determine the weighted matrices and the bias matrices, the systemdesigner sets up a neural network (see STEP 914). For example, theneural network may include a network that has 12 neurons (2 layers of 6neurons). In STEP 916 and as shown in FIG. 12 , the blower map 1020 andan activation function are used to train (e.g., determine the weightedmatrices and the bias matrices) the neural network estimator. In thisexemplary embodiment, the activation function is a Sigmoid

$\left( {{i.e.},{{{Sig}(z)} = {\frac{2}{1 + e^{{- 2}z}} + 1}}} \right).$Once the neural network estimator is trained (e.g., the weightedmatrices and the bias matrices are demined), these values may beprogramed into the ventilation device 310 as the air path parameters415, 425.

The performance of the ventilation device 310 will then be measuredusing this mathematical equations 500 a, 500 b. If the performance ofthe ventilation device 310 is acceptable, then the mathematical equation500 a, 500 b and its associated air path parameters 415, 425 are set tobe installed in like model units at the factory. However, if theperformance is not acceptable, STEPS 900-910 and 916-920 may be repeateduntil the performance of ventilation device 310 is acceptable. Mainly,the performance of the ventilation device 310 is acceptable if it iswithin 10% of being balanced, which requires the supply and exhaust airpaths within +/−5% of the measured air flow from the blower motor 330,360, as determined within the test environment 600. In an alternativeembodiment, the mathematical equations 500 a, 500 b for the supply flowcontroller 410 and the exhaust flow controller 420 that are programedinto the ventilation device 310 at the factory may be updated after theventilation device 310 is installed within the building or structure by:(i) a technician physically connecting a data cable to the ventilationdevice 310, (ii) by a technician using a local network (e.g., Bluetoothor Wi-Fi) to wireless connect to the ventilation device 310, or (iii) bya centralized controller connecting to the ventilation device 310 over adistributed network (e.g., cellular network). It should be understood,that in the input matrix for the mathematical equations 500 a, 500 b maybe altered to include other variables (e.g., temperature), otheractivation functions may be used (e.g., see the below table ofactivation functions), neurons may be added or subtracted from theneural network, or a different mathematical equation may be used thatprovides additional rewards and penalties to the network during itstraining.

FIGS. 14A-14B, 16, and 18-19 , provide exemplary graphs showing theperformance of the ventilation device 310 using the neural networkderived mathematical equations 500 a, 500 b set forth above in thesecond location, such as a house, apartment, or office. At this secondlocation, the user may set the user defined air flow set point 364 usinga controller to select the desired CFM output of the system 300 into theenvironment 305. The controller may be: (i) a wall controller that iscoupled to the ventilation device 310 using a wire, (ii) a wallcontroller that is coupled to the ventilation device 310 using awireless connection (e.g., Bluetooth, Wi-Fi), (iii) a mobile device thatis wirelessly coupled to the ventilation device 310, or (iv) acombination of any of these controllers. For example, the user mayspecify the user defined air flow set point 364 to a value between 40CFM to 300 CFM depending on the operating environment 305.

FIG. 14A displays the air flow of the ventilation device 310 graphsagainst estimated air flow error percentage, while FIG. 14B displays theair flow of the ventilation device 310 graphed against the estimated airflow error. As seen in the graphs contained within FIGS. 14A-14B, theventilation device 310 operates within +/−5% of the measured air flowfrom the blower motor 330, 360, as determined within the testenvironment 600. This meets and far exceeds the 10% requirement of thebuilding codes (e.g., Canada's National building codes, including9.32.3.4-9.32.3.5, and CAN/CSA-F326-M91). In addition, to meeting andexceeding the building code requirements, the ventilation device 310does not require manual balancing at the time of installation nor doesit require the ventilation device 310 to be rebalanced due to seasonchanges, which saves time and cost.

FIGS. 15A-15B, 16, and 17-18 , provide exemplary graphs showing theperformance of an air exchanger utilizing a polynomial equations. Inparticular, FIGS. 15A-15B and 17 show the performance of the airexchanger that utilizes a first order polynomial equation for speed ofthe motor and a second order polynomial equation for current of themotor, while FIGS. 18-19 show the performance of the air exchanger inconnection with various number of coefficient for various degree ofpolynomial equations. Review of the graphs provided in connection withFIGS. 15A-15B, show that the use of a polynomial equation does not meetthe building codes. Additionally, FIGS. 18-19 show that the ventilationdevice 310 that utilizes the neural network mathematical equation isapproximately ten times closer to being balanced in comparison to theair exchanger that utilizes low order polynomial equation (e.g., lessthan 6^(th) order). And even if an air exchanger could theoreticallyutilize a higher order polynomial equation (e.g., 18^(th) order) withoutthe polynomial equation becoming unstable, which usually occurs around a5^(th) order equation, the ventilation device 310 that utilizes theneural network approach is still approximately two times closer to beingbalanced. Accordingly, the use of a neural network in connection withthe ventilation device 310 is far superior to an air exchanger thatutilizes a polynomial equation approach.

FIGS. 1-14B, 16, 18-19 describe the use of a single neural network basedmathematical equations 500 a, 500 b and a single set of air flowparameters 415, 425 for each blower motor 385, 390. In alternativeembodiments, it may be desirable to have multiple mathematical equations500 a, 500 b and multiple sets of air flow parameters 415, 425 for eachblower motor 385, 390. This may be advantageous because eachmathematical equation and set of air flow parameters may be used in aspecific situation, such as for different air densities that can occurin different geographic locations where temperature, humidity,elevation, relative to sea level and atmospheric pressure can eachdiffer. For example, it may be advantageous to have at least fiveseparate mathematical equations and sets of air flow parameters, where:i) a first set is used when the ambient air between −20° to 0°, ii) asecond set is used when the ambient air between 1° to 20°, iii) a thirdset is used when the ambient air between 21 to 40, iv) a fourth set isused when the ambient air between 41° to 60°, and v) a fifth set is usedwhen the ambient air between 61° to 80°. In other embodiments, it may beadvantageous to have at least five separate mathematical equations andsets of air flow parameters, where: i) a first set is used when therelative humidity is between 0 to 20, ii) a second set is used when therelative humidity is between 21 to 40, iii) a third set is used when therelative humidity is between 41 to 60, iv) a fourth set is used when therelative humidity is between 61 to 80, and v) a fifth set is used whenthe relative humidity is between 81 to 100.

In other embodiments, it may be advantageous to have at least twentyseparate mathematical equations and sets of air flow parameters, whereeach set covers a different combination of temperature ranges andrelative humidity ranges. Other embodiments may include a motorizedproportional damper 395, such as the motorized damper disclosed withinSer. No. 16/242,498, filed on Jan. 9, 2019 to bring the pressure withinthe flow estimation operating limits. In other embodiments, it may bedesirable to have multiple mathematical equations and multiple sets ofair flow parameters to account for different air filters. For example,one mathematical equation and its air flow parameter may be used for anair filter having low flow characteristic and another mathematicalequation and its air flow parameter may be used for an air filter havinghigh flow characteristic. In even further alternative embodiment, aneural network may be utilized that can account for the temperaturechanges, whether the heat recovery core is operations, and whether anair handler or HVAC unit is installed, and the type of air filter thatis installed. This neural network will include additional inputs,additional neurons, and will require additional training over the neuralnetwork described above. It should also be understood that other neuronsor configurations of neurons may be utilized no matter the number ofinputs.

FIGS. 20A-2B describe a second embodiment of a ventilation device 1310that utilizes a mathematical equation (e.g., neural network basedequation) to accurately adjust air flow of the ventilation device 1310to meet a target air flow. It should be understood that this secondembodiment ventilation device 1310 contains structures, features and/orfunctions that are similar to the structures, features and/or functionsdisclosed in connection with the first embodiment of the ventilationdevice 310. Accordingly, reference numbers that are separated by 1000will be used in connection with this second embodiment to denote thestructures and/or features that are similar to the structures and/orfeatures disclosed in the first embodiment. Additionally, one ofordinary skill in the art shall understand that while the structures,features and/or functions are similar that does not mean the structures,features and/or functions are exactly the same. Further, it should beunderstood that structures and/or features of this second embodiment maybe used in connection with any other embodiment contained within thisapplication or its related applications.

Like the first embodiment of the ventilation device 310, the secondembodiment of the ventilation device 1310 includes: (i) two PMSM motor1385, 1390 and (ii) control circuitry 1340 that includes a neuralnetwork based mathematical equation 1500. Also, the second embodiment ofthe ventilation device 1310 also uses the mathematical equation 1500 toadjust the air flow rates of the motors 1385, 1390. Unlike the firstembodiment of the ventilation device 310, the second embodiment of theventilation device is range hood and the motors 1385, 1390 are designedto force air outside of the structure or building. Thus, the neuralnetwork estimators for the supply and the exhaust fans may be combineinto a single neural network estimator 1456. In addition, the firstcalculator 1429, 1249 a and flow controller 1433, 1433 a can be combine.Accordingly, the control circuitry 1340 does not attempt to balance theair flow from air stream 1320 with the air flow from the other airstream 1350. Nevertheless, the control circuitry 1340 still utilizes theneural network based mathematical equation 1500 to accurately adjust thevalues supplied to the motors 1385, 1390 to achieve the desired usertarget air flow rate 1364.

FIG. 21 describe a third embodiment of a ventilation device 2130 thatutilizes a mathematical equation (e.g., neural network based equation)to accurately adjust air flow of the ventilation device to meet a targetair flow. It should be understood that this third embodiment ventilationdevice 2310 contains structures, features and/or functions that aresimilar to the structures, features and/or functions disclosed inconnection with the first embodiment of the ventilation device 310.Accordingly, reference numbers that are separated by 2000 will be usedin connection with this second embodiment to denote the structuresand/or features that are similar to the structures and/or featuresdisclosed in the first embodiment. Additionally, one of ordinary skillin the art shall understand that while the structures, features and/orfunctions are similar that does not mean the structures, features and/orfunctions are exactly the same. Further, it should be understood thatstructures and/or features of this second embodiment may be used inconnection with any other embodiment contained within this applicationor its related applications.

Like the first embodiment of the ventilation device 310, the thirdembodiment of the ventilation device 2310 includes control circuitry1340 that includes a neural network based mathematical equation 2500.Also, the third embodiment of the ventilation device 2310 also uses themathematical equation 2500 to adjust the air flow rate of a motor 2390.Unlike the first embodiment of the ventilation device 310, the thirdembodiment of the ventilation device 2310 is bathroom fan and onlyincludes one motor 2390, which is designed to force air outside of thestructure or building. The neural network estimator for the supply fansmay be omitted. Accordingly, the control circuitry 2340 does not attemptto balance the air flow from air stream 2320 with the air flow from theother air stream 2350. Nevertheless, the control circuitry 2340 stillutilizes the neural network based mathematical equation 2500 toaccurately adjust the values supplied to the motor 2390 to achieve thedesired air flow rate 2364. It should be understood that the ventilationdevice may also be supply ventilators, and different types of fan basedproducts, such as liquid pumps.

INDUSTRIAL APPLICABILITY

The above disclosure may represent an improvement in the art because itdescribes an ventilation device 310 that automatically balances itselfusing mathematical equations. Additionally, the ventilation device 310has the ability to rebalance itself in light of temperatures changes orother like factors. Further, the ventilation device 310 that canautomatically balance itself within 10% error of being balanced alsorepresents an improvement in the art. Accordingly, the ventilationdevice 310 will save the installer from performing all the STEPSdescribed in FIG. 2A at various points, including installation and whenthe seasons change. In addition, the ventilation device 310 will meetvarious building codes (see Canada's National building codes). Further,the ventilation device 310 described herein is less expensive than othersolutions because it does not use flow sensors contained within the core325 of the ventilation device 310 to adjust the speed of the blowers330, 360.

While some implementations have been illustrated and described, numerousmodifications come to mind without significantly departing from thespirit of the disclosure, and the scope of protection is only limited bythe scope of the accompanying claims. For example, the cross-sectionalshape and cross-sectional area of the supply duct 122, supply ducting inthe building 135, exhaust ducting in the building 155, and exhaust duct160 as well as the material from which they are formed, can varydepending on the operating environment and the requirements of thesystem. For example, the ducting 122, 135, 155, 160 can be comprised ofrigid and/or flexible materials as generally known in the art.

In some embodiments, the motor 385, 390 can be a brushless AC (BLAC) orbrushless DC motor (BLDC). These types of motors are synchronouselectric motors powered by either alternating current (AC) or directcurrent (DC) electricity and having an electronic commutation system,rather than a mechanical commutator and brushes, which results inimproved motor efficiency and reduced mechanical wear, increasing thelife of the motor. Current to torque and voltage to rpm are linearrelationships in BLAC and BLDC motors. Brushless DC motors generallyexhibit a reduced operating noise as compared to other types of motorssuitable for driving a blower wheel or similar fan element. In additionto PMSM motors, BLDC motors provide reliable start-up and continualoperation and controllability at very low speeds. In some embodiments,an interface can be provided to convert an AC power signal which wouldbe used to control an AC induction motor to a usable input to control,PMSM, or BLDC motor. Of course, it should be understood by one of skillin the art that various embodiments of the invention can alternativelyutilize other types of motors.

In some embodiments, the ventilation device 310 may be connected via awire or wirelessly with other ventilation deices contained within theenvironment 305. These other ventilation devices may include rangehoods, supply fans, bathroom fans, or etc. Connecting these devices toone another may provide benefits where the bathroom fan can accuratelyaccount for the volume of air that was removed from the environment 305and a supply fan can be utilized to provide the volume of air that wasremoved. This will ensure that the environment 305 remains balanced. Inanother embodiment, the ventilation device 310 may be connected to anIndoor Air Quality controller, which may regulate when and how much airvolume is moved into or removed from the environment 305. One example ofan Indoor Air Quality controller is described within 62/789,501, whichwas filed on Jan. 7, 2019, which is hereby incorporated by referenceherein for all purposes.

It should also be understood that other equation and other activationfunctions for the neural network may be used. An example of otheractivation equations that may be used are shown in the below table. Tonote, the neural network may use any combination of these activationequations. For example, a ReLU activation function may be used incombination with Softmax function.

Name Equation Identity f( x) = x Binary step${f(x)} = \left\{ \begin{matrix}{0\mspace{14mu}} & {{{for}\mspace{14mu} x} < 0} \\{1\mspace{14mu}} & {{{for}\mspace{14mu} x} \geq 0}\end{matrix} \right.$ Sigmoid${f(x)} = {{\sigma(x)} = \frac{1}{1 + e^{- x}}}$ TanH${f(x)} = {{\tanh(x)} = \frac{\left( {e^{x} - e^{- x}} \right)}{\left( {e^{x} + e^{- x}} \right)}}$ArcTan f(x) = tan⁻¹ (x) Softsign ${f(x)} = \frac{x}{1 + {x}}$ Inversesquare root unit (ISRU) ${f(x)} = \frac{x}{\sqrt{1 + {\alpha\; x^{2}}}}$Rectified linear unit (ReLU) ${f(x)} = \left\{ \begin{matrix}{0\mspace{14mu}} & {{{for}\mspace{14mu} x} < 0} \\{x\mspace{14mu}} & {{{for}\mspace{14mu} x} \geq 0}\end{matrix} \right.$ Leaky rectified linear unit (Leaky ReLU)${f(x)} = \left\{ \begin{matrix}{{0.01\; x}\mspace{14mu}} & {{{for}\mspace{14mu} x} < 0} \\{x\mspace{14mu}} & {{{for}\mspace{14mu} x} \geq 0}\end{matrix} \right.$ Parameteric rectified linear unit (PReLU)${f\left( {\alpha,x} \right)} = \left\{ \begin{matrix}{{\alpha\; x}\mspace{14mu}} & {{{for}\mspace{14mu} x} < 0} \\{x\mspace{14mu}} & {{{for}\mspace{14mu} x} \geq 0}\end{matrix} \right.$ Randomized leaky rectified linear unit (RReLU)${f\left( {\alpha,x} \right)} = \left\{ \begin{matrix}{{\alpha\; x}\mspace{14mu}} & {{{for}\mspace{14mu} x} < 0} \\{x\mspace{14mu}} & {{{for}\mspace{14mu} x} \geq 0}\end{matrix} \right.$ Exponential linear unit (ELU)${f\left( {\alpha,x} \right)} = \left\{ \begin{matrix}{{\alpha\;\left( {e^{x} - 1} \right)}\mspace{14mu}} & {{{for}\mspace{14mu} x} < 0} \\{x\mspace{14mu}} & {{{for}\mspace{14mu} x} \geq 0}\end{matrix} \right.$ Scaled exponential linear unit (SELU)${f\left( {\alpha,x} \right)} = {\lambda\left\{ \begin{matrix}{{\alpha\;\left( {e^{x} - 1} \right)}\mspace{14mu}} & {{{for}\mspace{14mu} x} < 0} \\{x\mspace{14mu}} & {{{for}\mspace{14mu} x} \geq 0}\end{matrix} \right.}$ with λ = 1.0507 and α = 1.67326 S-shapedrectified linear activation unit (SReLU)${f_{t_{l},a_{l},t_{r},a_{r}}(x)} = \left\{ \begin{matrix}{t_{l} + {a_{l}\left( {x - t_{l}} \right)}} & {{{for}\mspace{14mu} x} \leq t_{l}} \\x & {{{for}\mspace{14mu} t_{l}} < x < t_{r}} \\{t_{r} + {a_{r}\left( {x - t_{r}} \right)}} & {{{for}\mspace{14mu} x} \geq t_{r}}\end{matrix} \right.$ t_(l), a_(l), t_(r), a_(r) are parameters Inversesquare root linear unit (ISRLU) ${f(x)} = \left\{ \begin{matrix}{\frac{x}{\sqrt{1 + {\alpha\; x^{2}}}}\mspace{14mu}} & {{{for}\mspace{14mu} x} < 0} \\{x\mspace{14mu}} & {{{for}\mspace{14mu} x} \geq 0}\end{matrix} \right.$ Adaptive piecewise linear (APL)${f(x)} = {{\max\mspace{11mu}\left( {0,x} \right)} + {\sum\limits_{s = 1}^{S}\;{a_{i}^{s}\mspace{11mu}\max\mspace{11mu}\left( {0,{{- x} + b_{i}^{s}}} \right)}}}$SoftPlus f(x) = ln (1 + e^(x)) Bent identity${f(x)} = {\frac{\sqrt{x^{2} + 1} - 1}{2} + x}$ Sigmoid-weighted f(x) =x · σ(x) linear unit (SiLU) SoftExponential${f\left( {\alpha,x} \right)} = \left\{ \begin{matrix}{- \frac{\ln\;\left( {1 - {\alpha\left( {x + \alpha} \right)}} \right)}{\alpha}} & {{{for}\mspace{14mu}\alpha} < 0} \\x & {{{for}\mspace{14mu}\alpha} = 0} \\{\frac{e^{\alpha\; x} - 1}{\alpha} + \alpha} & {{{for}\mspace{14mu}\alpha} > 0}\end{matrix} \right.$ Sinusoid f(x) = sin(x) Sinc${f(x)} = \left\{ \begin{matrix}{1\mspace{14mu}} & {{{for}\mspace{14mu} x} = 0} \\{\frac{\sin\mspace{11mu}(x)}{x}\mspace{14mu}} & {{{for}\mspace{14mu} x} \neq 0}\end{matrix} \right.$ Gaussian f(x) = e^(−x) ² Softmax${f_{i}\mspace{11mu}\left( \overset{\rightarrow}{x} \right)} = \frac{e^{x_{i}}}{\sum\limits_{j = 1}^{J}\; e^{x_{j}}}$for i = 1, . . . , J Maxout${f\mspace{11mu}\left( \overset{\rightarrow}{x} \right)} = {\max\limits_{i}\mspace{11mu} x_{i}}$

Headings and subheadings, if any, are used for convenience only and arenot limiting. The word exemplary is used to mean serving as an exampleor illustration. To the extent that the term include, have, or the likeis used, such term is intended to be inclusive in a manner similar tothe term comprise as comprise is interpreted when employed as atransitional word in a claim. Relational terms such as first and secondand the like may be used to distinguish one entity or action fromanother without necessarily requiring or implying any actual suchrelationship or order between such entities or actions.

Phrases such as an aspect, the aspect, another aspect, some aspects, oneor more aspects, an implementation, the implementation, anotherimplementation, some implementations, one or more implementations, anembodiment, the embodiment, another embodiment, some embodiments, one ormore embodiments, a configuration, the configuration, anotherconfiguration, some configurations, one or more configurations, thesubject technology, the disclosure, the present disclosure, othervariations thereof and alike are for convenience and do not imply that adisclosure relating to such phrase(s) is essential to the subjecttechnology or that such disclosure applies to all configurations of thesubject technology. A disclosure relating to such phrase(s) may apply toall configurations, or one or more configurations. A disclosure relatingto such phrase(s) may provide one or more examples. A phrase such as anaspect or some aspects may refer to one or more aspects and vice versa,and this applies similarly to other foregoing phrases.

Numerous modifications to the present disclosure will be apparent tothose skilled in the art in view of the foregoing description. Preferredembodiments of this disclosure are described herein, including the bestmode known to the inventors for carrying out the disclosure. It shouldbe understood that the illustrated embodiments are exemplary only, andshould not be taken as limiting the scope of the disclosure.

The invention claimed is:
 1. A ventilation system comprising: aventilation device configured to generate an air flow and including: afirst blower assembly including a blower motor and a control circuit,said control circuit programmed with a first mathematical equation;wherein the first mathematical equation determines an estimated blowerair flow for the first blower assembly based upon at least: (i) air pathparameters of the blower motor that are derived from the use of a neuralnetwork, (ii) a blower motor speed, and (iii) a blower motor current;wherein the air flow of the ventilation device is set to an air flow setpoint, the estimated blower air flow is within 5% of said air flow setpoint.
 2. The ventilation system of claim 1, wherein the control circuitprogrammed with a plurality of mathematical equations configured todetermine the estimated blower air flow for the first blower assembly,and wherein the plurality of math equations includes the firstmathematical equation.
 3. The ventilation system of claim 2, wherein onemathematical equation of the plurality of mathematical equations isselected by the control circuit based upon a set of operating parameter;and wherein operating parameter is selected from the following operatingparameters: (i) density of the air external to the ventilation system,(ii) temperature of air external to the ventilation system, (iii)humidity of air external to the ventilation system, (iv) anidentification of the type of an air filter installed in the ventilationsystem, (v) inclusion of a heat recovery core within the ventilationsystem, (vi) inclusion of an air handler, and (vii) inclusion of a HVACunit.
 4. The ventilation system of claim 1, wherein the ventilationdevice is capable of receiving an updated mathematical equation from aremote location, and wherein the control circuit is capable of replacingthe first mathematical equation with the updated mathematical equation.5. The ventilation system of claim 1, wherein the ventilation system isconfigured to be installed within an operating environment; and whereinthe ventilation device is capable of receiving information about either:(i) a volume of air added or removed from the operating environment byother ventilation devices that are installed within said operatingenvironment, or (ii) an indoor air quality controller that is installedwithin the operating environment.
 6. The ventilation system of claim 5,wherein the control circuit is configured to modify an air flow setpoint determined by a user of the ventilation system based upon theinformation received about the volume of air added or removed from theoperating environment.
 7. The ventilation system of claim 5, wherein thecontrol circuit configured to modify an air flow set point determined bya user of the ventilation system based upon the information receivedfrom the indoor air quality controller.
 8. The ventilation system ofclaim 1, wherein when the estimated blower air flow is different than anair flow set point determined by a user of the ventilation system, thecontrol circuit being configured to selectively alter power supplied tothe blower motor in order to make the estimated blower air flow equal tothe air flow set point.
 9. The ventilation system of claim 1, furtherincluding a motorized damper that is configured to be controlled by thecontrol circuit of the first blower assembly of the ventilation device.10. The ventilation system of claim 9, wherein the ventilation systemhas a static pressure; and wherein the motorized damper is configured tobring the static pressure within the operating limits of the firstblower assembly.
 11. A ventilation system comprising: a first blowerassembly including: (i) a blower motor, and (ii) a control circuitconfigured to determine an estimated blower air flow for the firstblower assembly based upon the following inputs: (a) air path parametersof the blower motor that are derived from the use of a neural network,(b) a blower motor speed, and (c) a blower motor current.
 12. Theventilation system of claim 11, wherein the ventilation system has astatic pressure; and wherein a motorized damper is configured to bringthe static pressure within the operating limits of the first blowerassembly.
 13. The ventilation system of claim 11, wherein the firstblower assembly further includes a current limit for the blower motor;and wherein a warning is provided when an air flow set point determinedby a user of the ventilation system is set to a value that requires thecurrent supplied to the blower motor to be greater than the currentlimit.
 14. The ventilation system of claim 11, wherein the controlcircuit of the first blower assembly is programmed with a plurality ofmathematical equations configured to determine the estimated blower airflow for the first blower assembly.
 15. The ventilation system of claim14, wherein one mathematical equation of the plurality of mathematicalequations is selected by the first blower assembly based upon a set ofoperating parameter.
 16. The ventilation system of claim 15, whereinoperating parameter is selected from the following operating parameters:(i) density of the air external to the ventilation system, (ii)temperature of air external to the ventilation system, (iii) humidity ofair external to the ventilation system, (iv) an identification of thetype of an air filter installed in the ventilation system, (v) inclusionof a heat recovery core within the ventilation system, (vi) inclusion ofan air handler, and (vii) inclusion of a HVAC unit.
 17. The ventilationsystem of claim 11, wherein the ventilation system is capable ofreceiving a plurality of updated air path parameters from a remotelocation, and wherein the first blower assembly is capable of replacingthe air path parameters with the plurality of updated air pathparameters.
 18. The ventilation system of claim 11, wherein theventilation system is configured to be installed within an operatingenvironment; and wherein the ventilation system is capable of receivinginformation about a volume of air added or removed from the operatingenvironment by other ventilation devices that are installed within saidoperating environment.
 19. The ventilation system of claim 18, whereinthe control circuit of the first blower assembly is configured to modifyan air flow set point determined by a user of the ventilation systembased upon the information received about the volume of air added orremoved from the operating environment.
 20. The ventilation system ofclaim 11, wherein the ventilation system is configured to be installedwithin an operating environment; and wherein the ventilation system iscapable of receiving information from an indoor air quality controllerthat is installed within the operating environment.
 21. The ventilationsystem of claim 20, wherein the control circuit of the first blowerassembly is configured to modify an air flow set point determined by auser of the ventilation system based upon the information received fromthe indoor air quality controller.
 22. The ventilation system of claim20, wherein when the estimated blower air flow is different than an airflow set point determined by a user of the ventilation system, the firstblower assembly being configured to selectively alter power supplied tothe blower motor in order to make the estimated blower air flow equal tothe air flow set point.